Using Machine Learning to Enhance Personality Prediction in Education

In recent years, there has been an increase in interest in using machine learning (ML) techniques for educational proposals and psychological science due to ML’s effective role in improving educational system services and academic performance. ML makes the learning process more effective, personalized, and accurate. Through ML, we can discover relevant and innovative uses in the education sector, for example, adaptive learning, virtual reality, learning styles, fraud detection, analyzing success indicators, reducing school failure, smart tutoring, and smart academic orientation. This paper systematically presents a comprehensive literature review of existing personality recognition techniques from a psychological and computational perspective. Specifically, the research covered in this paper concerns, on the one hand, the feasibility of using personality traits as indicators of educational success and, on the other hand, the classification of learners according to personality type to determine the learner’s learning style and the appropriate academic orientation using ML techniques.